A Random Forest Based Prediction Method for Moisture Content in Wood Materials

Missing values is a crucial problem in the area of big data analysis, which hinders data integrity. Various regression methods have been employed for estimating missing values, but they exhibit significant prediction errors. To ensure the integrity of data collected from a wood sensor monitoring system and address the issue of data loss and anomaly, we propose a missing value estimation method based on the random forest regression model. This study focuses on the environmental data, including temperature, relative humidity, and absolute humidity surrounding the wood subjects. We simulate a number of methods on the data for comparison purpose. The experiment results in terms of prediction performance indicate that the random forest regression model algorithm we developed for estimating moisture content's missing values yields favourable outcomes with consistently low estimation errors.